Claude

@claude

共 180 期 · 已译制 1 期

23:13

一年前,我们正式推出了Claude Code。这个最初作为内部项目诞生的、在终端中运行的智能编码工具,如今已被全球的开发者与组织广泛使用。 Claude Code负责人Boris Cherny与产品负责人Cat Wu共同回顾了Claude Code的第一年:从一个仅获两种反应的Slack演示,到工程团队将其部署至整个代码库。他们探讨了验证的最佳实践、自动模式背后的思考、各自钟爱的日常流程与循环、Claude Code在工程领域之外的采用、上下文极简主义的兴起,以及如何为AI指数级增长进行构建。 0:00 - Claude Code的起源与演进 1:10 - 如何让Claude擅长验证 3:14 - 角色融合:超越工程师的Claude Code 4:48 - 利用日常流程进行持续集成、代码审查等 6:43 - Boris钟爱的功能:自动模式 8:10 - 保护自动模式:红队测试与评估 10:24 - 为何循环是下一个飞跃 11:06 - 工程组织与职责如何变化 13:30 - 未来属于产品还是工程? 14:20 - 与数百个智能体协作:使用代理视图、语音模式与远程控制 16:05 - 从上下文工程到上下文极简主义 17:17 - Claude Code的下一个发展方向 了解更多关于Claude Code的内容:https://code.claude.com/docs/en/overview 关注X上的@ClaudeDevs,获取Claude Code团队的产品更新与最佳实践:https://x.com/ClaudeDevs

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See exactly how Claude works inside Microsoft Word. Reading a document, resolving reviewer comments, fact-checking claims, cutting length, and copy editing, all as tracked changes you approve along the way. Claude Academy → https://claude.com/learn Chapters: 0:00 Claude in the Word panel 0:18 Summarize the open comments 1:12 Turn on tracked changes 1:35 Check claims against the source doc in Box 2:26 Fix formatting and rewrite for the customer 3:18 Get under the word count 4:57 Run a final copy-edit pass with a skill

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See exactly how Claude works inside Microsoft Word. Reading a document, resolving reviewer comments, fact-checking claims, cutting length, and copy editing, all as tracked changes you approve along the way. Claude Academy → https://claude.com/learn Chapters: 0:00 Claude in the Word panel 0:18 Summarize the open comments 1:12 Turn on tracked changes 1:35 Check claims against the source doc in Box 2:26 Fix formatting and rewrite for the customer 3:18 Get under the word count 4:57 Run a final copy-edit pass with a skill

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0:59

Choose a Claude model based on the complexity of the task. Then adjust the effort level to move between faster or more thorough results.

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0:55

The information you share with AI only travels as far as you let it. Zoe from the Anthropic education team explains what happens to your information when you chat with an AI, how long it stays there, and how to take control of where it goes.

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The information you share with AI only travels as far as you let it. Zoe from the Anthropic education team explains what happens to your information when you chat with an AI, how long it stays there, and how to take control of where it goes. Have a question? Let us know in the comments. Learn more at Claude Academy: http://academy.claude.com Chapters 0:00 What does AI know about you? 1:05 The four places your data can go 1:25 Use 1: The conversation itself 1:48 Use 2: Product memory 2:19 Use 3: The provider's systems 2:48 Use 4: Training future models 3:33 Habits for staying in control

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0:42

Claude sees the page you're already signed in to and works on it: it reads, clicks, types, and fills forms. Your skills, plugins, and connectors work in the browser for the first time. Every conversation saves to your history, and sessions live with your account, not the machine. Start a task in a browser tab, then pick it up on your desktop or phone. Try it: claude.com/chrome

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0:50

How much you can trust an AI depends on what you’re asking. Kyra from the Anthropic education team breaks down the two most common reasons for an AI to be confidently wrong: hallucination and sycophancy.

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How much you can trust an AI depends on what you’re asking. Kyra from the Anthropic education team breaks down the two most common reasons for an AI to be confidently wrong: hallucination and sycophancy. Have a question? Let us know in the comments. Learn more at Claude Academy: http://academy.claude.com Chapters 0:00 Can you trust an AI's answer? 1:39 Hallucination and sycophancy, explained 3:04 Trust is a dial, not a switch 3:36 Four habits for checking AI

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0:44

AI模型一次只写一个词,但它并非一次只思考一个词。来自Anthropic用户体验团队的Jane剖析了每个AI输出背后的预测过程,以及如何更好地理解你得到的回复。 有问题吗?请在评论区告诉我们。 了解更多,请访问Claude学院:http://academy.claude.com

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21:59

Ramp在其整个工程生命周期中运行AI代理:编写代码、审查代码、监控生产环境以及根因分析事故。 Boris与Ramp的Austin Ray和Rahul Sengottuvelu坐下来讨论了如何达到这一目标。为即将到来的模型而非现有模型构建,为每位工程师提供无上限的智能访问权限,以及使其有效运行的护栏。他们比较了Claude Code的配置、循环与动态工作流,以及Claude Fable 5解锁了什么。 Claude Code:anthropic.com/product/claude-code Claude Cowork:anthropic.com/product/claude-cowork Office Hours LP:claude.com/office-hours 章节 0:00 "修复我们所有的导入循环" 0:32 在Ramp的Python模块上对Fable进行压力测试 1:33 Fable和动态工作流将CI时间缩短66% 3:36 长期任务中循环与动态工作流的对比 5:15 Claude Code配置:基础版 vs. 后台密集型 6:49 工程生命周期中的AI代理 7:23 为未来模型而非今日模型构建 9:11 AI代理护栏与最小权限 12:00 成本控制与AI代码审查 13:08 Ramp的实验文化 13:52 Glass和Inspect:Ramp的AI同事 16:05 待命助理:基于Claude Code的AI SRE 17:13 来自自动化的代理会话多于人类 18:44 工程师无令牌预算 20:48 给采用AI代理的CTO们的建议

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4:48

AI模型一次只写一个词,但它并非一次只思考一个词。来自Anthropic用户体验团队的Jane剖析了每个AI输出背后的预测过程,以及如何更好地理解你得到的回复。 有问题吗?请在评论区告诉我们。 了解更多,请访问Claude学院:http://academy.claude.com 章节 0:00 当你与AI对话时会发生什么? 1:07 AI训练如何工作 2:32 模型如何思考 3:37 获得更好结果的四个习惯

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5:42

自动模式让Claude Code以更少的干扰完成长时间运行的工作,通过一个独立的分类器代替你来筛选每个动作。本视频介绍了分类器的工作原理,为什么Claude从不批准自己的操作,以及如何为你的环境和团队配置自动模式。 阅读博客:https://claude.com/blog/auto-mode-default-in-claude-code 0:00 介绍 0:44 自动模式如何工作 3:33 配置自动模式 4:49 结束语

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1:42

有些任务你只需要做一次。 现在Claude Cowork的+菜单中有一个'记录技能'选项。

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1:05

AI模型并非无所不知。它们的训练在某些领域赋予了它们惊人的深度,同时也在其他领域造成了盲点。以下是如何区分它们。

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1:04

向AI询问某个具体统计数据,它可能会凭空编造一个。这些错误被称为幻觉,是AI在不知道答案时努力提供帮助的结果。

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0:58

AI模型是“成长”出来的,而非“构建”出来的。它们从人类文本中学习行为,再通过微调过程中的精选示例进一步塑造。

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什么是谄媚?

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一个总是同意你的模型并不是有用的模型。了解为什么谄媚很重要,如何在与AI聊天时发现它,以及我们正在采取哪些措施来减少模型中的谄媚现象。

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1:20

丹尼·科尔特斯制作纽约市细节的微缩模型,这些细节大多数人都会视而不见:杂货店、邮箱、垃圾箱、店面。对他来说,每一个生锈的角落和褪色的标志都值得保留。在Claude的帮助下,他将一张照片变成1:12比例的蓝图,精确还原每一个尺寸。

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2:58

高中教师扎克展示了他如何使用Claude for Teachers分析课堂数据,并构建差异化的教案,这些教案针对每个学生的水平量身定制,并与州立标准对接。

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1:31

当一群蜜蜂出现在某人的院子里时,奥尼克斯·贝尔德会接到电话。十年的再生养蜂经验让她学会了依靠直觉和大量信息。在Claude的帮助下,她将所有信息整合起来,将多年收集的问题变成一页指南,可以交给任何客户。

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2:47

小学教师卡琳娜分享了她如何使用Claude for Teachers获取每日反馈,并将这些指导自动融入教案中,所有内容都基于真实标准,通过连接TeachFX和学习共享知识图谱实现。

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2:58

高中教师扎克展示了他如何使用Claude for Teachers分析课堂数据,并构建差异化的教案,这些教案针对每个学生的水平量身定制,并与州立标准对接。

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1:44

Some of the world's leading researchers and pharmaceutical companies are now using Claude to accelerate their science. Last month, the people behind that work joined us to share what they've built -- and to launch Claude Science, an AI workbench for scientists. If you missed it, watch the session here:: https://www.anthropic.com/events/the-briefing-ai-for-science-virtual-event

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16:33

Agents are moving from tools you prompt to infrastructure that runs your business. But what does it take to run them in production? Jess Yann (Product Manager, Claude Managed Agents), Katelyn Lesse (Head of Engineering, Claude Platform), and Angela Jiang (Head of Product, Claude Platform) discuss how teams are building agentic infrastructure, including identity, permissions, memory, and agent-to-agent communication. They also share how organizations should think about agentic ROI and designing human-agent teams that adapt to evolving model intelligence. Learn more about the Claude Platform: https://claude.com/platform/api 0:00 Intro 1:00 - Building Claude Managed Agents in production 2:15 - How agents talk to each other 3:00 - The future of agentic infrastructure: thinner harnesses and adversarial agent pairs 8:20 - Barriers to agentic adoption: security, compliance, and evals 9:15 - How to measure agent ROI 12:45 - Failure modes: hyper independence and sprawl 13:30 - The future: agents as an invisible substrate 15:15 - What's next for the Claude Platform

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1:31

We don’t get the benefits of AI without addressing the hard questions. Share your own: https://claude.com/hard-questions All voices featured in this film are from real people we’ve spoken with. You can learn more about the initiative here: https://anthropic.com/news/hard-questions

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We don’t get the benefits of AI without addressing the hard questions. Share your own: https://claude.com/hard-questions All voices featured in this film are from real people we’ve spoken with. You can learn more about the initiative here: https://anthropic.com/news/hard-questions

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Danny Cortes makes miniatures of the New York City details most people walk right past: bodegas, mailboxes, dumpsters, storefronts. To him, every rusted corner and faded sign is worth preserving. With the help of Claude, he turns a single photo into a 1:12-scale blueprint, getting every dimension just right.

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0:48

Thomson Reuters has spent more than 150 years serving professions where being right is non-negotiable, like law, tax, and compliance. CTO Joel Hron explains how legal research went from a tedious, manual search problem to agentic deep research that retrieves relevant case law while also verifying every citation. That means faster answers lawyers can actually stand behind.

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